{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "3713a0e9",
   "metadata": {
    "ExecutionIndicator": {
     "show": true
    },
    "execution": {
     "iopub.execute_input": "2025-10-17T11:36:29.300357Z",
     "iopub.status.busy": "2025-10-17T11:36:29.300080Z",
     "iopub.status.idle": "2025-10-17T11:36:29.303498Z",
     "shell.execute_reply": "2025-10-17T11:36:29.303130Z",
     "shell.execute_reply.started": "2025-10-17T11:36:29.300343Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import torch\n",
    "from torch.utils.data import Dataset, DataLoader\n",
    "from modelscope import AutoModel, AutoTokenizer\n",
    "from sklearn.model_selection import train_test_split\n",
    "from torch import  nn,optim\n",
    "import torch.nn.functional as F\n",
    "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
    "model_name=\"google-bert/bert-base-uncased\"\n",
    "from transformers import get_scheduler\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7ec99333-a297-4e56-88da-6f6f5526e881",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "3e3127b9",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-10-17T11:35:04.677098Z",
     "iopub.status.busy": "2025-10-17T11:35:04.676648Z",
     "iopub.status.idle": "2025-10-17T11:35:05.162781Z",
     "shell.execute_reply": "2025-10-17T11:35:05.162350Z",
     "shell.execute_reply.started": "2025-10-17T11:35:04.677076Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# 读取数据\n",
    "df=pd.read_csv(\"cybersecurity_attacks.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "61be3673",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-10-17T11:35:06.883681Z",
     "iopub.status.busy": "2025-10-17T11:35:06.883356Z",
     "iopub.status.idle": "2025-10-17T11:35:06.913708Z",
     "shell.execute_reply": "2025-10-17T11:35:06.913329Z",
     "shell.execute_reply.started": "2025-10-17T11:35:06.883659Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['Timestamp', 'Source IP Address', 'Destination IP Address',\n",
      "       'Source Port', 'Destination Port', 'Protocol', 'Packet Length',\n",
      "       'Packet Type', 'Traffic Type', 'Payload Data', 'Malware Indicators',\n",
      "       'Anomaly Scores', 'Alerts/Warnings', 'Attack Type', 'Attack Signature',\n",
      "       'Action Taken', 'Severity Level', 'User Information',\n",
      "       'Device Information', 'Network Segment', 'Geo-location Data',\n",
      "       'Proxy Information', 'Firewall Logs', 'IDS/IPS Alerts', 'Log Source'],\n",
      "      dtype='object')\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Alerts/Warnings           20067\n",
       "IDS/IPS Alerts            20050\n",
       "Malware Indicators        20000\n",
       "Firewall Logs             19961\n",
       "Proxy Information         19851\n",
       "Source IP Address             0\n",
       "Destination IP Address        0\n",
       "Source Port                   0\n",
       "Timestamp                     0\n",
       "Traffic Type                  0\n",
       "Packet Type                   0\n",
       "Packet Length                 0\n",
       "Protocol                      0\n",
       "Destination Port              0\n",
       "Attack Type                   0\n",
       "Payload Data                  0\n",
       "Anomaly Scores                0\n",
       "Severity Level                0\n",
       "Action Taken                  0\n",
       "Attack Signature              0\n",
       "User Information              0\n",
       "Geo-location Data             0\n",
       "Network Segment               0\n",
       "Device Information            0\n",
       "Log Source                    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 查看数据相关信息\n",
    "print(df.columns)\n",
    "\n",
    "df.isnull().sum().sort_values(ascending=False)\n",
    "\n",
    "# df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "53641a7b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-10-17T11:35:09.882870Z",
     "iopub.status.busy": "2025-10-17T11:35:09.882572Z",
     "iopub.status.idle": "2025-10-17T11:35:09.937448Z",
     "shell.execute_reply": "2025-10-17T11:35:09.937036Z",
     "shell.execute_reply.started": "2025-10-17T11:35:09.882853Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# 空数据处理\n",
    "df['Alerts/Warnings']=df['Alerts/Warnings'].apply(lambda x:'None' if pd.isna(x) else x)\n",
    "df['IDS/IPS Alerts'] = df['IDS/IPS Alerts'].apply(lambda x: 'No Data' if pd.isna(x) else x)\n",
    "df['Malware Indicators'] = df['Malware Indicators'].apply(lambda x: 'No Detection' if pd.isna(x) else x)\n",
    "df['Firewall Logs'] = df['Firewall Logs'].apply(lambda x: 'No Data'if pd.isna(x) else x)\n",
    "df['Proxy Information'] = df['Proxy Information'].apply(lambda x: 'No Proxy Data' if pd.isna(x) else x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "87e0f7d6",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-10-17T11:35:11.795058Z",
     "iopub.status.busy": "2025-10-17T11:35:11.794753Z",
     "iopub.status.idle": "2025-10-17T11:35:11.983259Z",
     "shell.execute_reply": "2025-10-17T11:35:11.982847Z",
     "shell.execute_reply.started": "2025-10-17T11:35:11.795038Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "import re\n",
    "\n",
    "devices = [\n",
    "    r'Windows',\n",
    "    r'Linux',\n",
    "    r'Android',\n",
    "    r'iPad',\n",
    "    r'iPod',\n",
    "    r'iPhone',\n",
    "    r'Macintosh']\n",
    "\n",
    "\n",
    "def device_os_finder(user_agent):\n",
    "    for device in devices:\n",
    "        match_device = re.search(device, user_agent, re.I)  # re.I makes the search case-insensitive\n",
    "        if match_device:\n",
    "            return match_device.group()\n",
    "    return 'Unknown'\n",
    "\n",
    "# Extract device or OS\n",
    "df['Device'] = df['Device Information'].apply(device_os_finder)\n",
    "df['Browser'] = df['Device Information'].str.split('/').str[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "c1ae545a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-10-17T11:35:14.126345Z",
     "iopub.status.busy": "2025-10-17T11:35:14.125848Z",
     "iopub.status.idle": "2025-10-17T11:35:14.131698Z",
     "shell.execute_reply": "2025-10-17T11:35:14.131184Z",
     "shell.execute_reply.started": "2025-10-17T11:35:14.126328Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Source Port', 'Destination Port', 'Protocol', 'Packet Length',\n",
       "       'Packet Type', 'Traffic Type', 'Payload Data', 'Malware Indicators',\n",
       "       'Anomaly Scores', 'Alerts/Warnings', 'Attack Type', 'Attack Signature',\n",
       "       'Action Taken', 'Severity Level', 'Network Segment',\n",
       "       'Proxy Information', 'Firewall Logs', 'IDS/IPS Alerts', 'Device',\n",
       "       'Browser'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "del df['Source IP Address']\n",
    "del df['Destination IP Address']\n",
    "del df['Timestamp']\n",
    "del df['Log Source']\n",
    "del df['User Information']\n",
    "del df['Device Information']\n",
    "del df['Geo-location Data']\n",
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "2994f4fd",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-10-17T11:35:19.076117Z",
     "iopub.status.busy": "2025-10-17T11:35:19.075812Z",
     "iopub.status.idle": "2025-10-17T11:35:19.254391Z",
     "shell.execute_reply": "2025-10-17T11:35:19.253938Z",
     "shell.execute_reply.started": "2025-10-17T11:35:19.076099Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_949/1142659385.py:7: FutureWarning: \n",
      "\n",
      "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n",
      "\n",
      "  sns.barplot(x=device_counts.index, y=device_counts.values, palette='Set2')\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "\n",
    "# 获取设备信息的统计数量\n",
    "device_counts = df['Device'].value_counts()\n",
    "\n",
    "plt.figure(figsize=(8, 5))\n",
    "sns.barplot(x=device_counts.index, y=device_counts.values, palette='Set2')\n",
    "\n",
    "plt.title('Device  Distribution')\n",
    "plt.xlabel('Device Type')\n",
    "plt.ylabel('Count')\n",
    "plt.xticks(rotation=45, ha='right')\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "123648d0",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-10-17T11:35:22.825178Z",
     "iopub.status.busy": "2025-10-17T11:35:22.824582Z",
     "iopub.status.idle": "2025-10-17T11:35:22.945095Z",
     "shell.execute_reply": "2025-10-17T11:35:22.944660Z",
     "shell.execute_reply.started": "2025-10-17T11:35:22.825146Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Protocol Attack Type  COUNT\n",
      "0     ICMP        DDoS   4508\n",
      "6      UDP        DDoS   4482\n",
      "2     ICMP     Malware   4461\n",
      "1     ICMP   Intrusion   4460\n",
      "3      TCP        DDoS   4438\n",
      "5      TCP     Malware   4437\n",
      "8      UDP     Malware   4409\n",
      "7      UDP   Intrusion   4408\n",
      "4      TCP   Intrusion   4397\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "# Count occurrences of each combination of PROTOCOL and ATTACK TYPE\n",
    "protocol_attack_counts = df.groupby(['Protocol', 'Attack Type']).size().reset_index(name='COUNT')\n",
    "\n",
    "# Sort by count in descending order and take top 10 for better visibility\n",
    "top_10 = protocol_attack_counts.sort_values('COUNT', ascending=False).head(10)\n",
    "\n",
    "# Print a summary of the data\n",
    "print(top_10)\n",
    "\n",
    "# Create a pie chart\n",
    "#ax = attack_counts.plot(kind='bar', stacked=True, figsize=(12, 6))\n",
    "plt.plot(figsize=(12, 8))\n",
    "plt.pie(top_10['COUNT'], labels=top_10.apply(lambda x: f\"{x['Protocol']} - {x['Attack Type']}\", axis=1), \n",
    "        autopct='%1.1f%%', startangle=90, pctdistance=0.85)\n",
    "\n",
    "# Add a circle at the center to create a donut chart (optional)\n",
    "center_circle = plt.Circle((0,0), 0.70, fc='white')\n",
    "fig = plt.gcf()\n",
    "fig.gca().add_artist(center_circle)\n",
    "\n",
    "# Add title\n",
    "plt.title('Top 10 Protocol-Attack Type Combinations')\n",
    "\n",
    "# Add legend\n",
    "plt.legend(title='Protocol - Attack Type', loc='center left', bbox_to_anchor=(1, 0, 0.5, 1))\n",
    "\n",
    "# Adjust layout to prevent cutting off labels\n",
    "plt.tight_layout()\n",
    "\n",
    "# Show the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "1e5eb498",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Explore relationships between features and the target variable\n",
    "# Select some features for visualization. Adjust based on actual features in your dataset.\n",
    "features = ['Source Port', 'Destination Port', 'Packet Length', 'Anomaly Scores']\n",
    "\n",
    "for feature in features:\n",
    "    plt.figure(figsize=(10, 6))\n",
    "    sns.boxplot(x='Attack Type', y=feature, data=df)\n",
    "    plt.title(f'{feature} vs. Attack Type')\n",
    "    plt.xlabel('Attack Type')\n",
    "    plt.ylabel(feature)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "f64b3451",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-10-17T11:35:28.028168Z",
     "iopub.status.busy": "2025-10-17T11:35:28.027888Z",
     "iopub.status.idle": "2025-10-17T11:35:28.031276Z",
     "shell.execute_reply": "2025-10-17T11:35:28.030856Z",
     "shell.execute_reply.started": "2025-10-17T11:35:28.028153Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Source Port', 'Destination Port', 'Protocol', 'Packet Length',\n",
       "       'Packet Type', 'Traffic Type', 'Payload Data', 'Malware Indicators',\n",
       "       'Anomaly Scores', 'Alerts/Warnings', 'Attack Type', 'Attack Signature',\n",
       "       'Action Taken', 'Severity Level', 'Network Segment',\n",
       "       'Proxy Information', 'Firewall Logs', 'IDS/IPS Alerts', 'Device',\n",
       "       'Browser'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "7ea1666b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-10-17T11:35:30.358634Z",
     "iopub.status.busy": "2025-10-17T11:35:30.358358Z",
     "iopub.status.idle": "2025-10-17T11:35:33.354755Z",
     "shell.execute_reply": "2025-10-17T11:35:33.354318Z",
     "shell.execute_reply.started": "2025-10-17T11:35:30.358619Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# 创建映射字典\n",
    "label_mapping = {'Malware': 0, 'DDoS': 1, 'Intrusion': 2}\n",
    "\n",
    "# 构建输入文本（包含所有列的数据）\n",
    "texts = [\n",
    "    f\"Source Port: {row['Source Port']}. \"\n",
    "    f\"Destination Port: {row['Destination Port']}. \"\n",
    "    f\"Protocol: {row['Protocol']}. \"\n",
    "    f\"Packet Length: {row['Packet Length']}. \"\n",
    "    f\"Packet Type: {row['Packet Type']}. \"\n",
    "    f\"Traffic Type: {row['Traffic Type']}. \"\n",
    "    f\"Payload Data: {row['Payload Data']}. \"\n",
    "    f\"Malware Indicators: {row['Malware Indicators']}. \"\n",
    "    f\"Anomaly Scores: {row['Anomaly Scores']}. \"\n",
    "    f\"Alerts/Warnings: {row['Alerts/Warnings']}. \"\n",
    "    f\"Attack Signature: {row['Attack Signature']}. \"\n",
    "    f\"Action Taken: {row['Action Taken']}. \"\n",
    "    f\"Severity Level: {row['Severity Level']}. \"\n",
    "    f\"Network Segment: {row['Network Segment']}. \"\n",
    "    f\"Proxy Information: {row['Proxy Information']}. \"\n",
    "    f\"Firewall Logs: {row['Firewall Logs']}. \"\n",
    "    f\"IDS/IPS Alerts: {row['IDS/IPS Alerts']}. \"\n",
    "    f\"Device: {row['Device']}. \"\n",
    "    f\"Browser: {row['Browser']}.\"\n",
    "    for _, row in df.iterrows()\n",
    "]\n",
    "\n",
    "# 标签\n",
    "label = [label_mapping[row['Attack Type']] for _, row in df.iterrows()]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "32a3112c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-10-17T11:36:33.654535Z",
     "iopub.status.busy": "2025-10-17T11:36:33.654258Z",
     "iopub.status.idle": "2025-10-17T11:36:44.245558Z",
     "shell.execute_reply": "2025-10-17T11:36:44.245134Z",
     "shell.execute_reply.started": "2025-10-17T11:36:33.654521Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading Model from https://www.modelscope.cn to directory: /mnt/workspace/.cache/modelscope/models/google-bert/bert-base-uncased\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2025-10-17 19:36:36.465623: I tensorflow/core/util/port.cc:113] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.\n",
      "2025-10-17 19:36:36.737694: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n",
      "To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
      "2025-10-17 19:36:37.850091: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading Model from https://www.modelscope.cn to directory: /mnt/workspace/.cache/modelscope/models/google-bert/bert-base-uncased\n"
     ]
    }
   ],
   "source": [
    "# 记载bert模型和分词器\n",
    "bert_model = AutoModel.from_pretrained(model_name).to(device)\n",
    "bert_tokenizer = AutoTokenizer.from_pretrained(model_name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "aa7ce9dd",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-10-17T11:37:16.552203Z",
     "iopub.status.busy": "2025-10-17T11:37:16.551115Z",
     "iopub.status.idle": "2025-10-17T11:37:27.943032Z",
     "shell.execute_reply": "2025-10-17T11:37:27.942432Z",
     "shell.execute_reply.started": "2025-10-17T11:37:16.552181Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "最长的token长度为： 200\n"
     ]
    }
   ],
   "source": [
    "# 查看最长的token有多大\n",
    "max_len = 0\n",
    "for text in texts:\n",
    "    tokens = bert_tokenizer.tokenize(text)\n",
    "    if len(tokens) > max_len:\n",
    "        max_len = len(tokens)\n",
    "print(\"最长的token长度为：\", max_len)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "ee26e5c9",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-10-17T11:37:31.199452Z",
     "iopub.status.busy": "2025-10-17T11:37:31.198953Z",
     "iopub.status.idle": "2025-10-17T11:37:31.227438Z",
     "shell.execute_reply": "2025-10-17T11:37:31.227047Z",
     "shell.execute_reply.started": "2025-10-17T11:37:31.199435Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "训练集样本数: 0.7\n",
      "验证集样本数: 0.1\n",
      "测试集样本数: 0.2\n"
     ]
    }
   ],
   "source": [
    "# 先划分训练集 (70%) 和临时集 (30%)\n",
    "texts_train, texts_temp, labels_train, labels_temp = train_test_split(\n",
    "    texts, label, test_size=0.3, random_state=42, stratify=label\n",
    ")\n",
    "\n",
    "# 再把临时集划分为验证集 (10%) 和测试集 (20%)，比例是 1:2\n",
    "texts_val, texts_test, labels_val, labels_test = train_test_split(\n",
    "    texts_temp, labels_temp, test_size=2 / 3, random_state=42, stratify=labels_temp\n",
    ")\n",
    "\n",
    "# 检查划分比例\n",
    "print(\"训练集样本数:\", len(texts_train) / 40000)\n",
    "print(\"验证集样本数:\", len(texts_val) / 40000)\n",
    "print(\"测试集样本数:\", len(texts_test) / 40000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "20bd1899",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-10-17T11:37:33.211603Z",
     "iopub.status.busy": "2025-10-17T11:37:33.211318Z",
     "iopub.status.idle": "2025-10-17T11:37:33.238070Z",
     "shell.execute_reply": "2025-10-17T11:37:33.237594Z",
     "shell.execute_reply.started": "2025-10-17T11:37:33.211586Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([32, 200])\n",
      "torch.Size([32, 200])\n",
      "torch.Size([32])\n"
     ]
    }
   ],
   "source": [
    "\n",
    "\n",
    "\n",
    "max_length = 200  # 可以根据文本长度调整\n",
    "\n",
    "\n",
    "# 自定义 Dataset\n",
    "class TextDataset(Dataset):\n",
    "    def __init__(self, texts, labels, tokenizer, max_length=max_length):\n",
    "        self.texts = texts\n",
    "        self.labels = labels\n",
    "        self.tokenizer = tokenizer\n",
    "        self.max_length = max_length\n",
    "\n",
    "    def __len__(self):\n",
    "        return len(self.texts)\n",
    "\n",
    "    def __getitem__(self, idx):\n",
    "        text = str(self.texts[idx])\n",
    "        label = self.labels[idx]\n",
    "\n",
    "        # tokenizer 编码\n",
    "        encoding = self.tokenizer(\n",
    "            text,\n",
    "            truncation=True,\n",
    "            padding='max_length',\n",
    "            max_length=self.max_length,\n",
    "            return_tensors='pt',\n",
    "        )\n",
    "\n",
    "        # 返回 dict，方便 DataLoader collate\n",
    "        return {\n",
    "            'input_ids': encoding['input_ids'].squeeze(0),  # [seq_len]\n",
    "            'attention_mask': encoding['attention_mask'].squeeze(0),  # [seq_len]\n",
    "            'labels': torch.tensor(label, dtype=torch.long)\n",
    "        }\n",
    "\n",
    "\n",
    "# 选择合适 batch_size（典型 BERT 是 16/32，可根据 GPU 内存调整）\n",
    "batch_size = 32\n",
    "\n",
    "# 构建 Dataset\n",
    "train_dataset = TextDataset(texts_train, labels_train, bert_tokenizer, max_length)\n",
    "val_dataset = TextDataset(texts_val, labels_val, bert_tokenizer, max_length)\n",
    "test_dataset = TextDataset(texts_test, labels_test, bert_tokenizer, max_length)\n",
    "\n",
    "# 构建 DataLoader\n",
    "train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True)\n",
    "val_loader = DataLoader(val_dataset, batch_size=batch_size, shuffle=False)\n",
    "test_loader = DataLoader(test_dataset, batch_size=batch_size, shuffle=False)\n",
    "\n",
    "k = 0\n",
    "# 检查第一个 batch\n",
    "for batch in train_loader:\n",
    "    print(batch['input_ids'].shape)  # [batch_size, max_length]\n",
    "    print(batch['attention_mask'].shape)  # [batch_size, max_length]\n",
    "    print(batch['labels'].shape)  # [batch_size]\n",
    "    break\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "ce845b7d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-10-17T11:37:36.559527Z",
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     "iopub.status.idle": "2025-10-17T11:37:36.565048Z",
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     "shell.execute_reply.started": "2025-10-17T11:37:36.559511Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "class MyModel(nn.Module):\n",
    "    def __init__(self, bert_model, num_labels=3, dropout=0.3, unfreeze_last_n=0):\n",
    "        super(MyModel, self).__init__()\n",
    "        self.bert = bert_model\n",
    "        hidden_size = (\n",
    "            self.bert.config.hidden_size if hasattr(self.bert, \"config\") else 768\n",
    "        )\n",
    "\n",
    "        # classifier head: 两层 + dropout + LayerNorm\n",
    "        self.dropout = nn.Dropout(dropout)\n",
    "        self.fc1 = nn.Linear(hidden_size, hidden_size // 2)\n",
    "        self.act = nn.ReLU()\n",
    "        self.norm = nn.LayerNorm(hidden_size // 2)\n",
    "        self.fc2 = nn.Linear(hidden_size // 2, num_labels)\n",
    "\n",
    "        # 默认先冻结所有 bert 参数，再按需解冻最后 n 层\n",
    "        for p in self.bert.parameters():\n",
    "            p.requires_grad = False\n",
    "        if unfreeze_last_n > 0:\n",
    "\n",
    "            # 适配 BertModel 的 encoder 结构\n",
    "            for layer in self.bert.encoder.layer[-unfreeze_last_n:]:\n",
    "                for p in layer.parameters():\n",
    "                    p.requires_grad = True\n",
    "            # 解冻 pooler\n",
    "            if hasattr(self.bert, \"pooler\"):\n",
    "                for p in self.bert.pooler.parameters():\n",
    "                    p.requires_grad = True\n",
    "\n",
    "    def forward(self, input_ids, attention_mask):\n",
    "        # 正常前向：不再全局禁用梯度（是否真的训练 bert 由 requires_grad 控制）\n",
    "        outputs = self.bert(input_ids=input_ids, attention_mask=attention_mask)\n",
    "\n",
    "        cls_emb = outputs.pooler_output\n",
    "\n",
    "        x = self.dropout(cls_emb)\n",
    "        x = self.fc1(x)\n",
    "        x = self.act(x)\n",
    "        x = self.norm(x)\n",
    "        x = self.dropout(x)\n",
    "        x = self.fc2(x)\n",
    "        return x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "20fe81d7",
   "metadata": {
    "ExecutionIndicator": {
     "show": true
    },
    "execution": {
     "iopub.execute_input": "2025-10-17T11:54:43.942660Z",
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     "shell.execute_reply": "2025-10-17T11:54:43.952372Z",
     "shell.execute_reply.started": "2025-10-17T11:54:43.942644Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# 构建模型：示例解冻最后 2 层 bert（根据显存/数据量调整）\n",
    "unfreeze_last_n = 4\n",
    "my_model = MyModel(bert_model, num_labels=3, dropout=0.3, unfreeze_last_n=unfreeze_last_n).to(device)\n",
    "\n",
    "# 不同参数组：给 bert 和 head 不同学习率\n",
    "bert_lr = 2e-5\n",
    "head_lr = 2e-4\n",
    "bert_params = [p for n, p in my_model.named_parameters() if \"bert\" in n and p.requires_grad]\n",
    "head_params = [p for n, p in my_model.named_parameters() if \"bert\" not in n and p.requires_grad]\n",
    "\n",
    "optimizer = optim.AdamW([\n",
    "    {\"params\": bert_params, \"lr\": bert_lr},\n",
    "    {\"params\": head_params, \"lr\": head_lr}\n",
    "])\n",
    "\n",
    "loss_fuc = nn.CrossEntropyLoss()\n",
    "\n",
    "num_epochs = 3\n",
    "# 修正 num_training_steps：epochs * batches_per_epoch\n",
    "num_training_steps = num_epochs * len(train_loader)\n",
    "scheduler = get_scheduler(\n",
    "    name='linear',\n",
    "    optimizer=optimizer,\n",
    "    num_warmup_steps=0,\n",
    "    num_training_steps=num_training_steps\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "97a64675",
   "metadata": {
    "ExecutionIndicator": {
     "show": true
    },
    "execution": {
     "iopub.execute_input": "2025-10-17T11:54:46.652577Z",
     "iopub.status.busy": "2025-10-17T11:54:46.652075Z",
     "iopub.status.idle": "2025-10-17T11:54:46.659519Z",
     "shell.execute_reply": "2025-10-17T11:54:46.659126Z",
     "shell.execute_reply.started": "2025-10-17T11:54:46.652556Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "from tqdm import tqdm\n",
    "def train():\n",
    "    my_model.train()\n",
    "    best_val_acc = 0.0\n",
    "    save_path = \"best_model.pt\"\n",
    "\n",
    "   \n",
    "\n",
    "    for epoch in range(num_epochs):\n",
    "        \n",
    "        train_loss_total = 0.0\n",
    "        train_samples = 0\n",
    "        with tqdm(train_loader, desc=f\"Epoch {epoch+1}/{num_epochs} [Train]\", unit=\"batch\") as pbar:\n",
    "            for batch in pbar:\n",
    "                input_ids = batch['input_ids'].to(device)\n",
    "                attention_mask = batch['attention_mask'].to(device)\n",
    "                labels = batch['labels'].to(device)\n",
    "\n",
    "                optimizer.zero_grad()\n",
    "                out = my_model(input_ids, attention_mask)\n",
    "                loss = loss_fuc(out, labels)\n",
    "                loss.backward()\n",
    "                optimizer.step()\n",
    "                scheduler.step()\n",
    "\n",
    "                train_loss_total += loss.item() * labels.size(0)\n",
    "                train_samples += labels.size(0)\n",
    "\n",
    "                out_label = out.argmax(dim=1)\n",
    "                accuracy = (out_label == labels).sum().item() / labels.size(0)\n",
    "                lr = optimizer.param_groups[0]['lr']\n",
    "                pbar.set_postfix(loss=f\"{loss.item():.4f}\", acc=f\"{accuracy:.3f}\", lr=f\"{lr:.6f}\")\n",
    "\n",
    "        avg_train_loss = train_loss_total / train_samples\n",
    "        # ------------------- 验证 -------------------\n",
    "        my_model.eval()\n",
    "        val_loss_total = 0.0\n",
    "        val_correct = 0\n",
    "        val_samples = 0\n",
    "        with torch.no_grad():\n",
    "            for batch in tqdm(val_loader, desc=f\"Epoch {epoch+1}/{num_epochs} [Val]\", unit=\"batch\"):\n",
    "                input_ids = batch['input_ids'].to(device)\n",
    "                attention_mask = batch['attention_mask'].to(device)\n",
    "                labels = batch['labels'].to(device)\n",
    "\n",
    "                out = my_model(input_ids, attention_mask)\n",
    "                loss = loss_fuc(out, labels)\n",
    "                val_loss_total += loss.item() * labels.size(0)\n",
    "\n",
    "                out_label = out.argmax(dim=1)\n",
    "                val_correct += (out_label == labels).sum().item()\n",
    "                val_samples += labels.size(0)\n",
    "\n",
    "        val_loss = val_loss_total / val_samples\n",
    "        val_accuracy = val_correct / val_samples\n",
    "        print(f\"Epoch {epoch+1}: TrainLoss={avg_train_loss:.4f} ValLoss={val_loss:.4f} ValAcc={val_accuracy:.4f}\")\n",
    "\n",
    "        # 根据验证集保存最优模型（用于后续测试或调参）\n",
    "        if val_accuracy > best_val_acc:\n",
    "            best_val_acc = val_accuracy\n",
    "            torch.save(my_model.state_dict(), save_path)\n",
    "            print(f\"Saved best model with val_acc={best_val_acc:.4f}\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "47be1fe3",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-10-17T11:54:49.510324Z",
     "iopub.status.busy": "2025-10-17T11:54:49.510064Z",
     "iopub.status.idle": "2025-10-17T12:05:02.273184Z",
     "shell.execute_reply": "2025-10-17T12:05:02.272642Z",
     "shell.execute_reply.started": "2025-10-17T11:54:49.510308Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 1/3 [Train]: 100%|██████████| 875/875 [03:09<00:00,  4.61batch/s, acc=0.281, loss=1.1104, lr=0.000013]\n",
      "Epoch 1/3 [Val]: 100%|██████████| 125/125 [00:17<00:00,  7.24batch/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1: TrainLoss=1.1112 ValLoss=1.0988 ValAcc=0.3357\n",
      "Saved best model with val_acc=0.3357\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 2/3 [Train]: 100%|██████████| 875/875 [03:05<00:00,  4.72batch/s, acc=0.344, loss=1.0991, lr=0.000007]\n",
      "Epoch 2/3 [Val]: 100%|██████████| 125/125 [00:17<00:00,  7.31batch/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 2: TrainLoss=1.0997 ValLoss=1.0998 ValAcc=0.3327\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 3/3 [Train]: 100%|██████████| 875/875 [03:05<00:00,  4.72batch/s, acc=0.250, loss=1.0993, lr=0.000000]\n",
      "Epoch 3/3 [Val]: 100%|██████████| 125/125 [00:17<00:00,  7.30batch/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 3: TrainLoss=1.0990 ValLoss=1.0987 ValAcc=0.3327\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "# 训练模型\n",
    "train()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "5591e800",
   "metadata": {
    "ExecutionIndicator": {
     "show": true
    },
    "execution": {
     "iopub.execute_input": "2025-10-17T11:48:57.291625Z",
     "iopub.status.busy": "2025-10-17T11:48:57.291342Z",
     "iopub.status.idle": "2025-10-17T11:49:31.205744Z",
     "shell.execute_reply": "2025-10-17T11:49:31.205353Z",
     "shell.execute_reply.started": "2025-10-17T11:48:57.291608Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Testing: 100%|██████████| 250/250 [00:33<00:00,  7.37batch/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Accuracy: 0.3350\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "# 测试\n",
    "\n",
    "my_model.eval()\n",
    "test_correct = 0\n",
    "test_samples = 0\n",
    "with torch.no_grad():\n",
    "    for batch in tqdm(test_loader, desc=\"Testing\", unit=\"batch\"):\n",
    "        input_ids = batch['input_ids'].to(device)\n",
    "        attention_mask = batch['attention_mask'].to(device)\n",
    "        labels = batch['labels'].to(device)\n",
    "\n",
    "        out = my_model(input_ids, attention_mask)\n",
    "        out_label = out.argmax(dim=1)\n",
    "        test_correct += (out_label == labels).sum().item()\n",
    "        test_samples += labels.size(0)\n",
    "\n",
    "test_accuracy = test_correct / test_samples\n",
    "print(f\"Test Accuracy: {test_accuracy:.4f}\")"
   ]
  }
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